Search papers, labs, and topics across Lattice.
20 papers published across 1 lab.
Generating synthetic populations with full reproducibility from public data sources could revolutionize urban studies and demographic modeling.
Only 8.7% of reproducibility-relevant mutations in ML repositories are detected by current validation workflows, revealing a critical oversight in safeguarding research integrity.
Automatic reproducibility for 32% of Maven Central packages reveals critical flaws in manual curation efforts and sets a new standard for software reliability.
Trust and traceability in AI-enabled scientific discovery are now recognized as critical pillars for future scientific computing ecosystems.
Partitioning algorithms with similar entanglement costs can incur drastically different execution penalties, revealing hidden trade-offs that could reshape DQC compiler design.
Generating synthetic populations with full reproducibility from public data sources could revolutionize urban studies and demographic modeling.
Only 8.7% of reproducibility-relevant mutations in ML repositories are detected by current validation workflows, revealing a critical oversight in safeguarding research integrity.
Automatic reproducibility for 32% of Maven Central packages reveals critical flaws in manual curation efforts and sets a new standard for software reliability.
Trust and traceability in AI-enabled scientific discovery are now recognized as critical pillars for future scientific computing ecosystems.
Partitioning algorithms with similar entanglement costs can incur drastically different execution penalties, revealing hidden trade-offs that could reshape DQC compiler design.
Achieving near state-of-the-art performance for under $7K opens the door for cost-effective language model training accessible to the broader research community.
SAMpLE transforms the integration of machine learning in virtual prototyping, enabling seamless evaluation of diverse models without cumbersome re-implementation.
Claim-locked reporting boosts the accuracy of LLM-generated statistical reports by over 37%, ensuring that evidence integrity is maintained throughout the writing process.
VietAIDetector achieves superior detection of AI-generated Vietnamese text without requiring any domain-specific training data, setting a new standard for language-specific AI content verification.
Only 6.5% of neuro-symbolic AI studies can be reproduced from their published artifacts, exposing a severe reproducibility crisis in the field.
The open-source satellite software ecosystem is not only growing in popularity but is also marked by a surprising diversity of goals and programming languages that could redefine development practices in the field.
The first open-source compiler for transforming ternary quantized models into mask-programmed silicon could revolutionize how AI models are deployed on custom hardware.
User perceptions of conversational AI shift dramatically with each model release, revealing complex dynamics of excitement and backlash that can reshape public discourse.
HRV Studio achieves near-perfect agreement with leading HRV analysis tools, revolutionizing reproducibility in cardiovascular research.
ReproAgent achieves unprecedented accuracy in translating research papers into executable code, outperforming existing methods by leveraging a dual-channel contract system.
TianoForge slashes bug triage time from 11 days to just 7 minutes, revolutionizing efficiency in the TianoCore development community.
Major gaps in firmware security practices could leave the TianoCore community vulnerable, but targeted improvements could significantly enhance UEFI firmware integrity.
Missing information in bug reports can significantly delay resolution, but new template fields could streamline the triage process in the TianoCore community.
Iterative LLM feedback can significantly improve research software quality, revealing critical trade-offs that challenge conventional development practices.
Aegis, trained with CyberFactory, outperforms existing models by 22.8 points in cybersecurity tasks, showcasing the power of agentic learning from real-world vulnerabilities.
